Overview

Dataset statistics

Number of variables15
Number of observations1253
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory285.8 KiB
Average record size in memory233.6 B

Variable types

Categorical2
Numeric13

Alerts

FECHA_DEF has a high cardinality: 629 distinct values High cardinality
NEUMONIA is highly correlated with EDAD and 11 other fieldsHigh correlation
EDAD is highly correlated with NEUMONIA and 11 other fieldsHigh correlation
DIABETES is highly correlated with NEUMONIA and 11 other fieldsHigh correlation
EPOC is highly correlated with NEUMONIA and 11 other fieldsHigh correlation
ASMA is highly correlated with NEUMONIA and 11 other fieldsHigh correlation
INMUSUPR is highly correlated with NEUMONIA and 11 other fieldsHigh correlation
HIPERTENSION is highly correlated with NEUMONIA and 11 other fieldsHigh correlation
OTRA_COM is highly correlated with NEUMONIA and 11 other fieldsHigh correlation
CARDIOVASCULAR is highly correlated with NEUMONIA and 11 other fieldsHigh correlation
OBESIDAD is highly correlated with NEUMONIA and 11 other fieldsHigh correlation
RENAL_CRONICA is highly correlated with NEUMONIA and 11 other fieldsHigh correlation
TABAQUISMO is highly correlated with NEUMONIA and 11 other fieldsHigh correlation
CLASIFICACION_FINAL is highly correlated with NEUMONIA and 11 other fieldsHigh correlation
NEUMONIA is highly correlated with EDAD and 11 other fieldsHigh correlation
EDAD is highly correlated with NEUMONIA and 11 other fieldsHigh correlation
DIABETES is highly correlated with NEUMONIA and 11 other fieldsHigh correlation
EPOC is highly correlated with NEUMONIA and 11 other fieldsHigh correlation
ASMA is highly correlated with NEUMONIA and 11 other fieldsHigh correlation
INMUSUPR is highly correlated with NEUMONIA and 11 other fieldsHigh correlation
HIPERTENSION is highly correlated with NEUMONIA and 11 other fieldsHigh correlation
OTRA_COM is highly correlated with NEUMONIA and 11 other fieldsHigh correlation
CARDIOVASCULAR is highly correlated with NEUMONIA and 11 other fieldsHigh correlation
OBESIDAD is highly correlated with NEUMONIA and 11 other fieldsHigh correlation
RENAL_CRONICA is highly correlated with NEUMONIA and 11 other fieldsHigh correlation
TABAQUISMO is highly correlated with NEUMONIA and 11 other fieldsHigh correlation
CLASIFICACION_FINAL is highly correlated with NEUMONIA and 11 other fieldsHigh correlation
NEUMONIA is highly correlated with EDAD and 11 other fieldsHigh correlation
EDAD is highly correlated with NEUMONIA and 11 other fieldsHigh correlation
DIABETES is highly correlated with NEUMONIA and 11 other fieldsHigh correlation
EPOC is highly correlated with NEUMONIA and 11 other fieldsHigh correlation
ASMA is highly correlated with NEUMONIA and 11 other fieldsHigh correlation
INMUSUPR is highly correlated with NEUMONIA and 11 other fieldsHigh correlation
HIPERTENSION is highly correlated with NEUMONIA and 11 other fieldsHigh correlation
OTRA_COM is highly correlated with NEUMONIA and 11 other fieldsHigh correlation
CARDIOVASCULAR is highly correlated with NEUMONIA and 11 other fieldsHigh correlation
OBESIDAD is highly correlated with NEUMONIA and 11 other fieldsHigh correlation
RENAL_CRONICA is highly correlated with NEUMONIA and 11 other fieldsHigh correlation
TABAQUISMO is highly correlated with NEUMONIA and 11 other fieldsHigh correlation
CLASIFICACION_FINAL is highly correlated with NEUMONIA and 11 other fieldsHigh correlation
SEXO is highly correlated with CLASIFICACION_FINALHigh correlation
NEUMONIA is highly correlated with EDAD and 11 other fieldsHigh correlation
EDAD is highly correlated with NEUMONIA and 11 other fieldsHigh correlation
DIABETES is highly correlated with NEUMONIA and 11 other fieldsHigh correlation
EPOC is highly correlated with NEUMONIA and 11 other fieldsHigh correlation
ASMA is highly correlated with NEUMONIA and 11 other fieldsHigh correlation
INMUSUPR is highly correlated with NEUMONIA and 11 other fieldsHigh correlation
HIPERTENSION is highly correlated with NEUMONIA and 11 other fieldsHigh correlation
OTRA_COM is highly correlated with NEUMONIA and 11 other fieldsHigh correlation
CARDIOVASCULAR is highly correlated with NEUMONIA and 11 other fieldsHigh correlation
OBESIDAD is highly correlated with NEUMONIA and 11 other fieldsHigh correlation
RENAL_CRONICA is highly correlated with NEUMONIA and 11 other fieldsHigh correlation
TABAQUISMO is highly correlated with NEUMONIA and 11 other fieldsHigh correlation
CLASIFICACION_FINAL is highly correlated with SEXO and 12 other fieldsHigh correlation
FECHA_DEF is uniformly distributed Uniform

Reproduction

Analysis started2021-12-10 20:38:07.904319
Analysis finished2021-12-10 20:38:45.834455
Duration37.93 seconds
Software versionpandas-profiling v3.1.0
Download configurationconfig.json

Variables

FECHA_DEF
Categorical

HIGH CARDINALITY
UNIFORM

Distinct629
Distinct (%)50.2%
Missing0
Missing (%)0.0%
Memory size82.1 KiB
2021-09-30
 
2
2021-09-24
 
2
2021-10-27
 
2
2020-04-16
 
2
2020-11-07
 
2
Other values (624)
1243 

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5 ?
Unique (%)0.4%

Sample

1st row2020-03-18
2nd row2020-03-18
3rd row2020-03-20
4th row2020-03-22
5th row2020-03-23

Common Values

ValueCountFrequency (%)
2021-09-302
 
0.2%
2021-09-242
 
0.2%
2021-10-272
 
0.2%
2020-04-162
 
0.2%
2020-11-072
 
0.2%
2020-06-102
 
0.2%
2021-07-252
 
0.2%
2021-06-302
 
0.2%
2021-09-042
 
0.2%
2021-04-232
 
0.2%
Other values (619)1233
98.4%

Length

2021-12-10T14:38:45.904611image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2021-09-302
 
0.2%
2021-01-032
 
0.2%
2021-02-242
 
0.2%
2021-07-152
 
0.2%
2021-01-292
 
0.2%
2021-01-112
 
0.2%
2021-06-082
 
0.2%
2021-07-272
 
0.2%
2020-11-192
 
0.2%
2021-06-262
 
0.2%
Other values (619)1233
98.4%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

SEXO
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size76.6 KiB
Hombre
628 
Mujer
625 

Length

Max length6
Median length6
Mean length5.501197127
Min length5

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowHombre
2nd rowMujer
3rd rowHombre
4th rowHombre
5th rowMujer

Common Values

ValueCountFrequency (%)
Hombre628
50.1%
Mujer625
49.9%

Length

2021-12-10T14:38:46.024637image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-12-10T14:38:46.094488image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
ValueCountFrequency (%)
hombre628
50.1%
mujer625
49.9%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

NEUMONIA
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct506
Distinct (%)40.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean258.9209896
Minimum1
Maximum812
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size9.9 KiB
2021-12-10T14:38:46.199667image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile45.6
Q1120
median249
Q3360
95-th percentile537
Maximum812
Range811
Interquartile range (IQR)240

Descriptive statistics

Standard deviation161.6604268
Coefficient of variation (CV)0.6243619995
Kurtosis0.09545303296
Mean258.9209896
Median Absolute Deviation (MAD)120
Skewness0.6353828691
Sum324428
Variance26134.09359
MonotonicityNot monotonic
2021-12-10T14:38:46.354642image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
829
 
0.7%
688
 
0.6%
2828
 
0.6%
2307
 
0.6%
2537
 
0.6%
1527
 
0.6%
747
 
0.6%
857
 
0.6%
727
 
0.6%
1697
 
0.6%
Other values (496)1179
94.1%
ValueCountFrequency (%)
14
0.3%
25
0.4%
32
 
0.2%
41
 
0.1%
51
 
0.1%
61
 
0.1%
73
0.2%
81
 
0.1%
92
 
0.2%
131
 
0.1%
ValueCountFrequency (%)
8121
0.1%
7951
0.1%
7841
0.1%
7821
0.1%
7731
0.1%
7641
0.1%
7631
0.1%
7511
0.1%
7451
0.1%
7431
0.1%

EDAD
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct1217
Distinct (%)97.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12463.42139
Minimum56
Maximum37747
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size9.9 KiB
2021-12-10T14:38:46.529516image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum56
5-th percentile2189.6
Q15666
median12121
Q317319
95-th percentile25879.2
Maximum37747
Range37691
Interquartile range (IQR)11653

Descriptive statistics

Standard deviation7737.418553
Coefficient of variation (CV)0.6208101541
Kurtosis0.01706119907
Mean12463.42139
Median Absolute Deviation (MAD)5615
Skewness0.5938021081
Sum15616667
Variance59867645.87
MonotonicityNot monotonic
2021-12-10T14:38:46.674400image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
226043
 
0.2%
76332
 
0.2%
70472
 
0.2%
115992
 
0.2%
45032
 
0.2%
22502
 
0.2%
36592
 
0.2%
28442
 
0.2%
150142
 
0.2%
19412
 
0.2%
Other values (1207)1232
98.3%
ValueCountFrequency (%)
561
0.1%
591
0.1%
611
0.1%
831
0.1%
1001
0.1%
1121
0.1%
1161
0.1%
1392
0.2%
1431
0.1%
1781
0.1%
ValueCountFrequency (%)
377471
0.1%
375771
0.1%
373281
0.1%
372261
0.1%
370091
0.1%
366361
0.1%
363041
0.1%
353221
0.1%
352061
0.1%
351241
0.1%

DIABETES
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct709
Distinct (%)56.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean436.6855547
Minimum1
Maximum1868
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size9.9 KiB
2021-12-10T14:38:46.834368image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile61
Q1187
median391
Q3621
95-th percentile1017.2
Maximum1868
Range1867
Interquartile range (IQR)434

Descriptive statistics

Standard deviation307.1605377
Coefficient of variation (CV)0.7033906536
Kurtosis0.8220393278
Mean436.6855547
Median Absolute Deviation (MAD)213
Skewness0.9124112776
Sum547167
Variance94347.59593
MonotonicityNot monotonic
2021-12-10T14:38:46.989554image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
786
 
0.5%
616
 
0.5%
2656
 
0.5%
1086
 
0.5%
865
 
0.4%
2915
 
0.4%
735
 
0.4%
4995
 
0.4%
5465
 
0.4%
5645
 
0.4%
Other values (699)1199
95.7%
ValueCountFrequency (%)
14
0.3%
34
0.3%
43
0.2%
62
0.2%
82
0.2%
102
0.2%
112
0.2%
122
0.2%
171
 
0.1%
212
0.2%
ValueCountFrequency (%)
18681
0.1%
17561
0.1%
17241
0.1%
15891
0.1%
14471
0.1%
14421
0.1%
14341
0.1%
14041
0.1%
14031
0.1%
13831
0.1%

EPOC
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct757
Distinct (%)60.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean502.0247406
Minimum1
Maximum2030
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size9.9 KiB
2021-12-10T14:38:47.154688image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile72
Q1225
median462
Q3715
95-th percentile1132.2
Maximum2030
Range2029
Interquartile range (IQR)490

Descriptive statistics

Standard deviation344.4080121
Coefficient of variation (CV)0.6860379264
Kurtosis0.730253287
Mean502.0247406
Median Absolute Deviation (MAD)246
Skewness0.8632385163
Sum629037
Variance118616.8788
MonotonicityNot monotonic
2021-12-10T14:38:47.434429image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
996
 
0.5%
1216
 
0.5%
1146
 
0.5%
3275
 
0.4%
945
 
0.4%
6765
 
0.4%
1205
 
0.4%
2435
 
0.4%
3005
 
0.4%
1405
 
0.4%
Other values (747)1200
95.8%
ValueCountFrequency (%)
11
 
0.1%
23
0.2%
33
0.2%
43
0.2%
51
 
0.1%
82
0.2%
101
 
0.1%
111
 
0.1%
122
0.2%
131
 
0.1%
ValueCountFrequency (%)
20301
0.1%
20041
0.1%
19471
0.1%
17161
0.1%
16841
0.1%
16441
0.1%
16321
0.1%
16131
0.1%
15661
0.1%
15501
0.1%

ASMA
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct755
Distinct (%)60.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean503.9225858
Minimum1
Maximum2035
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size9.9 KiB
2021-12-10T14:38:47.594211image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile73.6
Q1222
median460
Q3714
95-th percentile1144.6
Maximum2035
Range2034
Interquartile range (IQR)492

Descriptive statistics

Standard deviation346.2178504
Coefficient of variation (CV)0.6870457093
Kurtosis0.7970130385
Mean503.9225858
Median Absolute Deviation (MAD)246
Skewness0.888872956
Sum631415
Variance119866.7999
MonotonicityNot monotonic
2021-12-10T14:38:47.744336image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
46
 
0.5%
956
 
0.5%
816
 
0.5%
2125
 
0.4%
5635
 
0.4%
1965
 
0.4%
2725
 
0.4%
5585
 
0.4%
1005
 
0.4%
4195
 
0.4%
Other values (745)1200
95.8%
ValueCountFrequency (%)
11
 
0.1%
23
0.2%
46
0.5%
61
 
0.1%
82
 
0.2%
101
 
0.1%
123
0.2%
142
 
0.2%
182
 
0.2%
211
 
0.1%
ValueCountFrequency (%)
20351
0.1%
20231
0.1%
19601
0.1%
17621
0.1%
17271
0.1%
17021
0.1%
15651
0.1%
15531
0.1%
15471
0.1%
15451
0.1%

INMUSUPR
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct738
Distinct (%)58.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean506.0925778
Minimum2
Maximum2042
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size9.9 KiB
2021-12-10T14:38:47.904318image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile74
Q1224
median462
Q3718
95-th percentile1157.8
Maximum2042
Range2040
Interquartile range (IQR)494

Descriptive statistics

Standard deviation345.9558265
Coefficient of variation (CV)0.6835820988
Kurtosis0.7252845166
Mean506.0925778
Median Absolute Deviation (MAD)248
Skewness0.864915738
Sum634134
Variance119685.4339
MonotonicityNot monotonic
2021-12-10T14:38:48.059651image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
747
 
0.6%
976
 
0.5%
4895
 
0.4%
8305
 
0.4%
925
 
0.4%
3885
 
0.4%
1225
 
0.4%
1145
 
0.4%
805
 
0.4%
2145
 
0.4%
Other values (728)1200
95.8%
ValueCountFrequency (%)
24
0.3%
31
 
0.1%
45
0.4%
61
 
0.1%
82
 
0.2%
91
 
0.1%
123
0.2%
131
 
0.1%
141
 
0.1%
171
 
0.1%
ValueCountFrequency (%)
20421
0.1%
20181
0.1%
19591
0.1%
16981
0.1%
16621
0.1%
16441
0.1%
16211
0.1%
16141
0.1%
15731
0.1%
15601
0.1%

HIPERTENSION
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct688
Distinct (%)54.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean416.2585794
Minimum1
Maximum1850
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size9.9 KiB
2021-12-10T14:38:48.244374image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile55
Q1177
median371
Q3598
95-th percentile972.4
Maximum1850
Range1849
Interquartile range (IQR)421

Descriptive statistics

Standard deviation295.9029208
Coefficient of variation (CV)0.710863236
Kurtosis1.127985552
Mean416.2585794
Median Absolute Deviation (MAD)205
Skewness0.9833046605
Sum521572
Variance87558.53851
MonotonicityNot monotonic
2021-12-10T14:38:48.394347image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
778
 
0.6%
1217
 
0.6%
3926
 
0.5%
1196
 
0.5%
4116
 
0.5%
3865
 
0.4%
2005
 
0.4%
5985
 
0.4%
4755
 
0.4%
505
 
0.4%
Other values (678)1195
95.4%
ValueCountFrequency (%)
13
0.2%
24
0.3%
32
0.2%
42
0.2%
61
 
0.1%
82
0.2%
92
0.2%
101
 
0.1%
111
 
0.1%
122
0.2%
ValueCountFrequency (%)
18501
0.1%
17321
0.1%
17141
0.1%
15751
0.1%
14821
0.1%
14071
0.1%
13981
0.1%
13911
0.1%
13381
0.1%
13351
0.1%

OTRA_COM
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct820
Distinct (%)65.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean600.6121309
Minimum1
Maximum2478
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size9.9 KiB
2021-12-10T14:38:48.554536image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile74
Q1243
median537
Q3860
95-th percentile1357
Maximum2478
Range2477
Interquartile range (IQR)617

Descriptive statistics

Standard deviation428.0352277
Coefficient of variation (CV)0.7126649724
Kurtosis0.9360552387
Mean600.6121309
Median Absolute Deviation (MAD)306
Skewness0.919792499
Sum752567
Variance183214.1561
MonotonicityNot monotonic
2021-12-10T14:38:48.704654image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
967
 
0.6%
2317
 
0.6%
45
 
0.4%
2075
 
0.4%
6545
 
0.4%
975
 
0.4%
1665
 
0.4%
1214
 
0.3%
7054
 
0.3%
3214
 
0.3%
Other values (810)1202
95.9%
ValueCountFrequency (%)
11
 
0.1%
23
0.2%
45
0.4%
61
 
0.1%
72
 
0.2%
91
 
0.1%
111
 
0.1%
122
 
0.2%
131
 
0.1%
141
 
0.1%
ValueCountFrequency (%)
24781
0.1%
24131
0.1%
24041
0.1%
23951
0.1%
21791
0.1%
21181
0.1%
20201
0.1%
20061
0.1%
20011
0.1%
19971
0.1%

CARDIOVASCULAR
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct740
Distinct (%)59.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean502.7039106
Minimum1
Maximum2011
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size9.9 KiB
2021-12-10T14:38:48.864465image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile73
Q1220
median464
Q3711
95-th percentile1142.6
Maximum2011
Range2010
Interquartile range (IQR)491

Descriptive statistics

Standard deviation343.3287706
Coefficient of variation (CV)0.6829641928
Kurtosis0.6194867016
Mean502.7039106
Median Absolute Deviation (MAD)246
Skewness0.8411999569
Sum629888
Variance117874.6447
MonotonicityNot monotonic
2021-12-10T14:38:49.029327image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
998
 
0.6%
947
 
0.6%
5367
 
0.6%
46
 
0.5%
5726
 
0.5%
5766
 
0.5%
1536
 
0.5%
2595
 
0.4%
755
 
0.4%
6925
 
0.4%
Other values (730)1192
95.1%
ValueCountFrequency (%)
11
 
0.1%
23
0.2%
46
0.5%
61
 
0.1%
71
 
0.1%
81
 
0.1%
91
 
0.1%
101
 
0.1%
122
 
0.2%
131
 
0.1%
ValueCountFrequency (%)
20111
0.1%
18891
0.1%
18521
0.1%
17301
0.1%
17011
0.1%
16721
0.1%
16101
0.1%
15411
0.1%
15301
0.1%
15231
0.1%

OBESIDAD
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct714
Distinct (%)57.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean449.8747007
Minimum1
Maximum1888
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size9.9 KiB
2021-12-10T14:38:49.184539image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile64
Q1199
median404
Q3631
95-th percentile1014.8
Maximum1888
Range1887
Interquartile range (IQR)432

Descriptive statistics

Standard deviation311.4409945
Coefficient of variation (CV)0.6922838604
Kurtosis0.9874545479
Mean449.8747007
Median Absolute Deviation (MAD)218
Skewness0.925003129
Sum563693
Variance96995.49307
MonotonicityNot monotonic
2021-12-10T14:38:49.334702image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3897
 
0.6%
5196
 
0.5%
936
 
0.5%
4656
 
0.5%
2146
 
0.5%
3586
 
0.5%
685
 
0.4%
845
 
0.4%
865
 
0.4%
1245
 
0.4%
Other values (704)1196
95.5%
ValueCountFrequency (%)
13
0.2%
22
0.2%
33
0.2%
42
0.2%
51
 
0.1%
71
 
0.1%
81
 
0.1%
92
0.2%
102
0.2%
121
 
0.1%
ValueCountFrequency (%)
18881
0.1%
18511
0.1%
17601
0.1%
17221
0.1%
15031
0.1%
14472
0.2%
14391
0.1%
14341
0.1%
14261
0.1%
14171
0.1%

RENAL_CRONICA
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct757
Distinct (%)60.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean495.1851556
Minimum1
Maximum2082
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size9.9 KiB
2021-12-10T14:38:49.504558image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile71.6
Q1217
median448
Q3705
95-th percentile1135.4
Maximum2082
Range2081
Interquartile range (IQR)488

Descriptive statistics

Standard deviation341.103365
Coefficient of variation (CV)0.6888400453
Kurtosis0.7689435349
Mean495.1851556
Median Absolute Deviation (MAD)244
Skewness0.8822430826
Sum620467
Variance116351.5056
MonotonicityNot monotonic
2021-12-10T14:38:49.664299image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1146
 
0.5%
1376
 
0.5%
5546
 
0.5%
3755
 
0.4%
5875
 
0.4%
965
 
0.4%
2405
 
0.4%
1065
 
0.4%
4785
 
0.4%
915
 
0.4%
Other values (747)1200
95.8%
ValueCountFrequency (%)
11
 
0.1%
23
0.2%
31
 
0.1%
45
0.4%
61
 
0.1%
82
 
0.2%
91
 
0.1%
124
0.3%
131
 
0.1%
141
 
0.1%
ValueCountFrequency (%)
20821
0.1%
20021
0.1%
18281
0.1%
16921
0.1%
16611
0.1%
16171
0.1%
16081
0.1%
15491
0.1%
15371
0.1%
15311
0.1%

TABAQUISMO
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct750
Distinct (%)59.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean498.6256983
Minimum2
Maximum2062
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size9.9 KiB
2021-12-10T14:38:49.944598image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile73.6
Q1219
median458
Q3703
95-th percentile1123.4
Maximum2062
Range2060
Interquartile range (IQR)484

Descriptive statistics

Standard deviation341.004698
Coefficient of variation (CV)0.6838891359
Kurtosis0.8528495173
Mean498.6256983
Median Absolute Deviation (MAD)242
Skewness0.8787988415
Sum624778
Variance116284.204
MonotonicityNot monotonic
2021-12-10T14:38:50.104640image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
46
 
0.5%
3735
 
0.4%
3515
 
0.4%
3265
 
0.4%
915
 
0.4%
1105
 
0.4%
5555
 
0.4%
5185
 
0.4%
1155
 
0.4%
2965
 
0.4%
Other values (740)1202
95.9%
ValueCountFrequency (%)
24
0.3%
31
 
0.1%
46
0.5%
71
 
0.1%
81
 
0.1%
101
 
0.1%
112
 
0.2%
121
 
0.1%
131
 
0.1%
141
 
0.1%
ValueCountFrequency (%)
20621
0.1%
19931
0.1%
19011
0.1%
18611
0.1%
18241
0.1%
17051
0.1%
15961
0.1%
15671
0.1%
15661
0.1%
15231
0.1%

CLASIFICACION_FINAL
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct775
Distinct (%)61.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean570.1747805
Minimum2
Maximum1678
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size9.9 KiB
2021-12-10T14:38:50.244382image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile105.6
Q1269
median550
Q3786
95-th percentile1162.6
Maximum1678
Range1676
Interquartile range (IQR)517

Descriptive statistics

Standard deviation347.0110815
Coefficient of variation (CV)0.608604753
Kurtosis-0.111881788
Mean570.1747805
Median Absolute Deviation (MAD)259
Skewness0.5533832436
Sum714429
Variance120416.6907
MonotonicityNot monotonic
2021-12-10T14:38:50.404470image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2326
 
0.5%
1716
 
0.5%
65
 
0.4%
4585
 
0.4%
6765
 
0.4%
5525
 
0.4%
6965
 
0.4%
5655
 
0.4%
6134
 
0.3%
2114
 
0.3%
Other values (765)1203
96.0%
ValueCountFrequency (%)
22
 
0.2%
32
 
0.2%
41
 
0.1%
65
0.4%
81
 
0.1%
91
 
0.1%
121
 
0.1%
152
 
0.2%
182
 
0.2%
191
 
0.1%
ValueCountFrequency (%)
16781
0.1%
16701
0.1%
16611
0.1%
16531
0.1%
16481
0.1%
16451
0.1%
15951
0.1%
15901
0.1%
15851
0.1%
15741
0.1%

Interactions

2021-12-10T14:38:43.294442image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-12-10T14:38:11.244523image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-12-10T14:38:21.289482image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-12-10T14:38:24.492469image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-12-10T14:38:26.454764image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-12-10T14:38:28.424389image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-12-10T14:38:30.234493image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-12-10T14:38:32.164406image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-12-10T14:38:34.119376image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-12-10T14:38:35.844722image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-12-10T14:38:37.854386image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-12-10T14:38:39.874294image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-12-10T14:38:41.649488image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-12-10T14:38:43.424554image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-12-10T14:38:11.854785image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-12-10T14:38:21.434432image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-12-10T14:38:24.714441image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-12-10T14:38:26.594528image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-12-10T14:38:28.564579image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-12-10T14:38:30.384385image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-12-10T14:38:32.309415image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-12-10T14:38:34.254302image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-12-10T14:38:35.984466image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-12-10T14:38:38.044617image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-12-10T14:38:40.014634image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-12-10T14:38:41.774417image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-12-10T14:38:43.564244image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-12-10T14:38:19.714386image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-12-10T14:38:21.674717image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-12-10T14:38:24.875319image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-12-10T14:38:26.734325image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-12-10T14:38:28.699306image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-12-10T14:38:30.654501image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-12-10T14:38:32.454398image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-12-10T14:38:34.384433image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-12-10T14:38:36.124392image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-12-10T14:38:38.224567image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-12-10T14:38:40.144653image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-12-10T14:38:41.894449image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-12-10T14:38:43.709652image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-12-10T14:38:19.860544image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-12-10T14:38:21.814636image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-12-10T14:38:25.037317image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-12-10T14:38:26.864492image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-12-10T14:38:28.844744image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-12-10T14:38:30.799587image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-12-10T14:38:32.584724image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-12-10T14:38:34.524583image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-12-10T14:38:36.255590image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-12-10T14:38:38.384499image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-12-10T14:38:40.264711image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-12-10T14:38:42.024434image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-12-10T14:38:43.844631image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-12-10T14:38:20.004741image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-12-10T14:38:22.014622image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-12-10T14:38:25.184575image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-12-10T14:38:27.014663image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-12-10T14:38:28.994611image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-12-10T14:38:30.944778image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-12-10T14:38:32.724637image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-12-10T14:38:34.654508image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-12-10T14:38:36.404689image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-12-10T14:38:38.534626image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-12-10T14:38:40.394572image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-12-10T14:38:42.144324image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-12-10T14:38:43.984277image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-12-10T14:38:20.164536image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-12-10T14:38:22.425275image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-12-10T14:38:25.324453image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-12-10T14:38:27.284385image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-12-10T14:38:29.134476image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-12-10T14:38:31.084364image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-12-10T14:38:32.884418image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-12-10T14:38:34.804454image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-12-10T14:38:36.555917image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-12-10T14:38:38.674511image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-12-10T14:38:40.529519image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-12-10T14:38:42.284669image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-12-10T14:38:44.124569image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-12-10T14:38:20.314430image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-12-10T14:38:22.929946image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-12-10T14:38:25.474663image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-12-10T14:38:27.424290image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-12-10T14:38:29.284727image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-12-10T14:38:31.224612image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-12-10T14:38:33.044420image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-12-10T14:38:34.934411image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-12-10T14:38:36.704619image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-12-10T14:38:38.824449image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-12-10T14:38:40.662028image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-12-10T14:38:42.414569image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-12-10T14:38:44.484438image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-12-10T14:38:20.454700image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-12-10T14:38:23.362226image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-12-10T14:38:25.614610image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-12-10T14:38:27.584783image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-12-10T14:38:29.414727image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-12-10T14:38:31.364435image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-12-10T14:38:33.184560image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-12-10T14:38:35.074692image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-12-10T14:38:36.864750image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-12-10T14:38:38.964617image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-12-10T14:38:40.784545image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-12-10T14:38:42.544600image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-12-10T14:38:44.634256image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-12-10T14:38:20.589592image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-12-10T14:38:23.536425image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-12-10T14:38:25.774302image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-12-10T14:38:27.714708image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-12-10T14:38:29.550594image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-12-10T14:38:31.494582image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-12-10T14:38:33.314543image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-12-10T14:38:35.214304image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-12-10T14:38:37.004693image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-12-10T14:38:39.084673image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-12-10T14:38:41.034621image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-12-10T14:38:42.674548image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-12-10T14:38:44.784258image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-12-10T14:38:20.734342image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-12-10T14:38:23.674415image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-12-10T14:38:25.934531image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-12-10T14:38:27.874369image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-12-10T14:38:29.694667image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-12-10T14:38:31.634793image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-12-10T14:38:33.459175image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-12-10T14:38:35.354413image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-12-10T14:38:37.144681image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-12-10T14:38:39.244180image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-12-10T14:38:41.164633image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-12-10T14:38:42.804421image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-12-10T14:38:44.939204image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-12-10T14:38:20.874629image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-12-10T14:38:24.015971image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-12-10T14:38:26.064706image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-12-10T14:38:28.034226image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-12-10T14:38:29.824409image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-12-10T14:38:31.764415image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-12-10T14:38:33.594600image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-12-10T14:38:35.484452image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-12-10T14:38:37.424373image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-12-10T14:38:39.494192image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-12-10T14:38:41.289569image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-12-10T14:38:42.934498image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-12-10T14:38:45.064432image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-12-10T14:38:21.014753image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-12-10T14:38:24.179389image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-12-10T14:38:26.194731image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-12-10T14:38:28.154333image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-12-10T14:38:29.954338image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-12-10T14:38:31.894590image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-12-10T14:38:33.719326image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-12-10T14:38:35.594542image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-12-10T14:38:37.574294image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-12-10T14:38:39.624647image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-12-10T14:38:41.404536image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-12-10T14:38:43.044241image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-12-10T14:38:45.194217image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-12-10T14:38:21.144571image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-12-10T14:38:24.339323image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-12-10T14:38:26.319595image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-12-10T14:38:28.284579image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-12-10T14:38:30.074472image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-12-10T14:38:32.024661image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-12-10T14:38:33.964695image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-12-10T14:38:35.714688image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-12-10T14:38:37.709493image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-12-10T14:38:39.744552image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-12-10T14:38:41.514593image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
2021-12-10T14:38:43.169572image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Correlations

2021-12-10T14:38:50.564767image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2021-12-10T14:38:50.794535image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2021-12-10T14:38:51.034656image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2021-12-10T14:38:51.254523image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2021-12-10T14:38:45.434468image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
A simple visualization of nullity by column.
2021-12-10T14:38:45.724669image/svg+xmlMatplotlib v3.5.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

FECHA_DEFSEXONEUMONIAEDADDIABETESEPOCASMAINMUSUPRHIPERTENSIONOTRA_COMCARDIOVASCULAROBESIDADRENAL_CRONICATABAQUISMOCLASIFICACION_FINAL
02020-03-18Hombre211634443443446
12020-03-18Mujer183111219812222
22020-03-20Hombre15612222221123
32020-03-22Hombre213933432444344
42020-03-23Mujer16112221121223
52020-03-24Hombre317845664665648
62020-03-25Hombre211233442243436
72020-03-26Hombre634510121212912129121218
82020-03-26Mujer213934442442446
92020-03-27Hombre74041213141414141413131420

Last rows

FECHA_DEFSEXONEUMONIAEDADDIABETESEPOCASMAINMUSUPRHIPERTENSIONOTRA_COMCARDIOVASCULAROBESIDADRENAL_CRONICATABAQUISMOCLASIFICACION_FINAL
12432021-12-04Hombre643281841021011008397100939495153
12442021-12-04Mujer3618574353565344535651545584
12452021-12-05Hombre7536918710510811084106105102103102164
12462021-12-05Mujer46223348666868436866596367102
12472021-12-06Hombre632979174192191189170188188184185186144
12482021-12-06Mujer52246159717374507373677271109
12492021-12-07Hombre45234955687069516969656466105
12502021-12-07Mujer28141831374038251323932374060
12512021-12-08Hombre8315688867788812
12522021-12-08Mujer136321116181711181814181827